P99-1056 semi-automatic procedure similar to the rule-learning algorithm developed by Coltheart
W02-1819 comparable . In this paper , a rule-learning approach is proposed to predict
W02-1819 Abstract This paper describes a rule-learning approach towards Chinese prosodic
W04-2422 chunks , POS tags , or lemmas . The rule-learning system must determine which values
W02-1819 discusses the feature selection and rule-learning experiments in detail . Section
W02-1819 our linguistic knowledge . Hence rule-learning also helps us mine knowledge
S10-1017 rules is extracted with C4 .5 rule-learning algorithm ( Quinlan , 1993 )
P00-1017 further slowdown an already slow rule-learning module . 2.2 Overall Results
W02-1819 linguistic information and to apply rule-learning algorithms to automatically induce
W02-1819 follows . Section 2 introduces the rule-learning algorithms we used . Section
W11-1903 constraints was extracted with the C4 .5 rule-learning algorithm ( Quinlan , 1993 )
P08-1078 rule r , which is provided by the rule-learning algorithm ( see next section
N13-1128 AdventureWorks domain . The RIPPER rule-learning algorithm ( Cohen , 1995 ) achieved
W02-1819 based methods , which justifies rule-learning as an effective alternative to
W02-1819 and evaluate . Thus two typical rule-learning algorithms ( C4 .5 induction
W02-1819 avoid sparse data problem while rule-learning does n't have the restriction
W02-1819 labelling is often relatively small . Rule-learning is just suitable for this task
N06-3006 learning experiments using RIPPER , a rule-learning al - gorithm . The EPSaT corpus
W02-1819 learning ( Brill , 1995 ) are typical rule-learning algorithms that have been applied
W05-1308 Kumlien 1999 ) explored an automatic rule-learning approach that uses a combination
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